Brain
Image Analysis
Research Group

New instruments for imaging human brain
activity, such as fMRI, offer
a wonderful opportunity to study mechanisms in the brain.

Our group develops statistical
machine learning algorithms to analyze
fMRI data. We are specifically interested in algorithms that can learn
to identify and track the cognitive processes that give rise to
observed fMRI data.

Watch the video
demonstration of our computer program decoding which candidate word a person is
thinking about, based only on the neural activity captured in their
fMRI data. The program was trained using fMRI data from other
people, indicating that our different brains encode word meanings in
quite similar ways.

What the data look like:
In one fMRI study we trained our algorithms to decode
whether the words being read by a human subject were about tools,
buildings, food, or several other semantic categories. The trained
classifier is 90% accurate, for example, discriminating whether the
subject is reading words about tools or buildings.

The following figure shows, for
each of three different subjects, the
degree to which different brain locations can help predict the word's
semantic category. Red and yellow voxels are most predictive. Note
the most predictive regions in different subjects are in similar
locations.

Acknowledgements: We thank the W.M.Keck
Foundation and the U.S. National Science Foundation for their generous
support of this research.